Conventional language learning methodologies organize learning material into lessons, which may often contain metalinguistic instructional information followed by educationally-oriented exercises. Having pupils demonstrate their knowledge of particular subject matter in various educational activities comprising some series of questions is known. Also known are educational activities in which pupils demonstrate their knowledge by engaging in various tasks. In many cases, educational software may implement educational activities in which pupils demonstrate their knowledge through a series of questions or by engaging in various tasks.
Learning activities are ordinarily prepared manually by a teacher, textbook author, or other curriculum planner. Learning activities are commonly prepared by an instructor, educational expert, or some other party who is preparing educational content and who is also familiar with aspects of language learning. Typically, once these learning activities are generated they are then reproduced en masse. Similarly, conventional distance learning programs rely on prepackaged language learning software or traditional textbooks. In such prepackaged software and textbooks, curriculum planners create content and then try to have the same content serve many people. A problem with this paradigm is that learning activities are not dynamically generated to suit the needs of a language learner.
Conventional language-learning software and tools employ a teaching methodology, curriculum, and coursework that will remain static unless a replacement or supplemental textbook or language learning product is purchased by a language learner or developed by an instructor. Not only is a curriculum not adaptable but, with each page or new computer assignment, there is no adaptability of activities and learning materials on a more granular scale. The overall curriculum is static, so daily activities and skills exercises cannot adapt to address the learner's needs. For example, a learner's past tense verb conjugation may not be a problem, but the learner's spelling may be weak.
Conventional language methodologies often implement distractors, which are pre-determined incorrect answers to academic questions, such as multiple-choice problems. However, conventional language learning methodologies implement distractors that are entirely static and cannot adaptably address learners' weaknesses of particular skills. Moreover, known language learning tools are not adaptable to suit a learner's goals or content preferences. Learners are left to work with the static language learning materials supplied to them, regardless of learners' personal interests or the real-life applicability of the those materials in the context of the learners' goals.
What is needed is an efficient and effective way to dynamically create content and learning materials, or learning activities, for a language learning course. What is also needed is a way to dynamically generate learning activities and language learning coursework that may even adapt automatically mid-course, to suit a language learner's needs, goals, and interests.
As discussed above, developing distractors for the purposes of language learning is known in the art. However, distractors are commonly prepared in some wrote or manual manner so that they may be reproduced in volume. In many cases, when developing learning activities based upon a certain text, the activities must focus on some set of words to exercise various learning objectives. Usually, a person preparing learning activities manually identifies and develops the words upon which learning activities will be based or focused.
What is needed is a way, in a language learning context, to automatically identify words in text, which may be useful to language learning, and then extract those words from text. What is needed is a way to extract useful words of text, or keywords, and then store those keywords for use in developing learning activities.
Identifying keywords in a text to generate a summary of the content of the text is known. Conventional keyword extractors are typically interested in obtaining a percentage of keywords to provide a summary of the text as efficiently as possible. For efficiency, conventional keyword extraction tools seek to summarize the content of the text using as few keywords as possible. But, such conventional keyword extraction tools may incidentally filter out keywords that would otherwise be helpful for language learning, rendering conventional keyword extraction tools ineffective to language learning contexts.
What is needed for language learning is a way to obtain many keywords from text, regardless of whether keywords appear inefficient in summarization contexts. What is also needed is a way to extract and store keywords that are useful for language learning.
There are known means for identifying various attributes of a word in text, e.g., noun, past-tense, first-person, etc. It is also known to filter out various inconsequential words from a line of text. For example, it is known to remove articles such as “the” or “a” from an online search query through the use of a stop word list. Conversely, words which are rare in a corpus, having a lower probability of occurring in a given document, can be given weight in an information retrieval system through known techniques such as TF-IDF.
What is needed is a means for identifying and quantifying the pedagogical value of keywords extracted from text for language learning. What is needed is a way to look at a word's attributes to determine whether the word may help a pupil learn a language.
There are known methods in the art for calculating text difficulty. Conventional methods for calculating the difficulty of a corpus are directed toward adult native language speakers. Conventional methodologies of calculating text difficulty is usually done holistically by teachers and textbook writers. But, there is no established method of calculating a text difficulty that is tailored for second language learners. For native-language readers, and for children learning to read in their first language, there are accepted benchmarks determining text difficulty. There are known systems of qualifying books on levels, such as leveled readers from A-to-Z, something that most schoolchildren learn while learning to read.
Moreover, there are many known readability scales, such as Flesch-Kincaid, which measures the number of words in a sentence and the number of words total. But, known text difficulty calculation methods are designed for learners who already speak the language that they are then learning to read. When teaching a second language to non-native speakers, particularly young adults and adults—many of whom already know how to read in their native language, but simply do not know how to read in the second language—there are different challenges that make reading texts in the second language difficult for them when compared to the schoolchildren learning their native language.
Known first-language text difficulty levels and calculations are not always applicable in situations of adult second-language learners, or non-native speakers. First-language learners are learning to read as a skill, and learning to read is something that takes years, whereas second-language learners typically know how to read in their native language.
What is needed is a means of learning a language that applies a means different from known means of learning a native language. Typically schoolchildren are learning that the word “ball” maps to the concept that they already have for the entity, a ball. Adult language learners, on the other hand, already know how the words may map to certain concepts. So rather than having them start with “that is a ball,” adult language learners could start with something inherently more complicated, like “that is an electron,” or “I×ll take a half-caff latte with organic soy milk.” Adult second-language learners typically know how to read complicated items in their own languages. It is desirable for the adult learner to understand these items in a second-language.
Known text difficulty calculation methods may measure text difficulty in comparison to native speakers through their development from childhood. For second-language learners the scale should be different. For example, a non-native speaker may review a long block of text containing very complicated ideas in the second-language, but he or she will have no problem understanding the concepts. This is particularly true if the text contains complicated ideas but is written in a way that exercises simplified language. The ideas are no less complicated. On the other hand, a simple concept like the weather forecast may incomprehensible for a novice language learner if it is written in a linguistically challenging way. This is because the issues second-language learners have are not the same as first-language learners.
What is needed is a way to calculate a text difficulty of a corpus for non-native speakers. What is needed is a way to calculate a text difficulty of a corpus that is more suited for adult language learners. Text difficulty must be calculated primarily according to the idiosyncrasies of the language that make learning that language difficult. What is also needed is a way to automatically determine the appropriateness of text based on the calculated difficulty to then automatically prepare language learning coursework.
Generating distractors for educational exercises is known in the art. Distractors are ordinarily written manually by humans to prepare for various forms of language learning materials, such as a textbook or problems that exercise skills. Afterwards, the attendant language learning materials can be reproduced en masse. Usually, a teacher or other curricula expert writes questions about the form or content of a resource and then comes up with answers. This means that the teaching methodology, curriculum, and coursework will remain static unless a different textbook or language learning product is purchased. This manual effort is also inefficient and costly.
What is needed is a way to dynamically generate distractors for language leaning. What is needed is a way to adapt distraction generation and selection based on learner's needs. What is needed is a way to automatically generate distractors from a resource. What is also needed is a way to prepare distractors that may adapt to the various types of resources (e.g., a document containing text, an audio playback, a video playback) used for preparing learning exercises.